feat(kserve): install KServe
This commit is contained in:
Masaki Yatsu
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examples/kserve-mlflow-iris/01-train-and-register.ipynb
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examples/kserve-mlflow-iris/01-train-and-register.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Iris Classification with MLflow and KServe\n",
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"\n",
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"This notebook demonstrates:\n",
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"1. Training a simple scikit-learn model on the Iris dataset\n",
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"2. Logging the model to MLflow\n",
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"3. Preparing the model for deployment with KServe"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 1. Setup and Install Dependencies"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Install required packages\n",
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"!pip install mlflow scikit-learn boto3 -q"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import mlflow\n",
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"import mlflow.sklearn\n",
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"from sklearn.datasets import load_iris\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.linear_model import LogisticRegression\n",
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"from sklearn.metrics import accuracy_score, classification_report"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": "## 2. Configure MLflow\n\nSet MLflow tracking URI and authentication credentials."
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": "# MLflow configuration\nMLFLOW_TRACKING_URI = os.getenv('MLFLOW_TRACKING_URI', 'http://mlflow.mlflow.svc.cluster.local')\nmlflow.set_tracking_uri(MLFLOW_TRACKING_URI)\n\n# MLflow authentication\nos.environ['MLFLOW_TRACKING_USERNAME'] = os.getenv('MLFLOW_TRACKING_USERNAME', '')\nos.environ['MLFLOW_TRACKING_PASSWORD'] = os.getenv('MLFLOW_TRACKING_PASSWORD', '')\n\nprint(f\"MLflow Tracking URI: {MLFLOW_TRACKING_URI}\")\nprint(f\"MLflow Username: {os.environ['MLFLOW_TRACKING_USERNAME']}\")"
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 3. Load and Prepare Data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Load Iris dataset\n",
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"iris = load_iris()\n",
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"X = iris.data\n",
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"y = iris.target\n",
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"\n",
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"# Split data\n",
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"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
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"\n",
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"print(f\"Training samples: {len(X_train)}\")\n",
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"print(f\"Test samples: {len(X_test)}\")\n",
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"print(f\"Classes: {iris.target_names}\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 4. Train Model with MLflow Tracking"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Set experiment\n",
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"experiment_name = \"iris-classification\"\n",
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"mlflow.set_experiment(experiment_name)\n",
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"\n",
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"# Start MLflow run\n",
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"with mlflow.start_run(run_name=\"logistic-regression\") as run:\n",
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" # Train model\n",
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" model = LogisticRegression(max_iter=200, random_state=42)\n",
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" model.fit(X_train, y_train)\n",
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" \n",
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" # Predictions\n",
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" y_pred = model.predict(X_test)\n",
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" accuracy = accuracy_score(y_test, y_pred)\n",
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" \n",
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" # Log parameters\n",
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" mlflow.log_param(\"model_type\", \"LogisticRegression\")\n",
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" mlflow.log_param(\"max_iter\", 200)\n",
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" \n",
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" # Log metrics\n",
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" mlflow.log_metric(\"accuracy\", accuracy)\n",
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" \n",
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" # Log model\n",
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" mlflow.sklearn.log_model(\n",
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" model, \n",
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" \"model\",\n",
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" registered_model_name=\"iris-classifier\"\n",
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" )\n",
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" \n",
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" print(f\"\\nRun ID: {run.info.run_id}\")\n",
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" print(f\"Accuracy: {accuracy:.4f}\")\n",
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" print(f\"\\nClassification Report:\")\n",
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" print(classification_report(y_test, y_pred, target_names=iris.target_names))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 5. Get Model Information for KServe Deployment"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": "# Get the latest version of the registered model\nclient = mlflow.tracking.MlflowClient()\nmodel_name = \"iris-classifier\"\n\n# Use search_model_versions instead of deprecated get_latest_versions\nmodel_versions = client.search_model_versions(f\"name='{model_name}'\")\nlatest_version = max(model_versions, key=lambda x: int(x.version))\n\nprint(f\"\\n=== Model Information for KServe ===\")\nprint(f\"Model Name: {model_name}\")\nprint(f\"Version: {latest_version.version}\")\nprint(f\"Run ID: {latest_version.run_id}\")\nprint(f\"\\nArtifact URI: {latest_version.source}\")\nprint(f\"\\nUse this information to configure the KServe InferenceService.\")"
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 6. Test Local Prediction"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Test with a sample\n",
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"sample_input = [[5.1, 3.5, 1.4, 0.2]] # Should predict 'setosa'\n",
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"prediction = model.predict(sample_input)\n",
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"predicted_class = iris.target_names[prediction[0]]\n",
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"\n",
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"print(f\"\\nTest Input: {sample_input[0]}\")\n",
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"print(f\"Predicted Class: {predicted_class}\")\n",
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"print(f\"\\nThis sample will be used to test the KServe deployment.\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Next Steps\n",
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"\n",
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"1. Note the Model Name and Version from above\n",
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"2. Deploy the model using KServe with the InferenceService YAML\n",
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"3. Test the deployed model endpoint"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.0"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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